Shuzhen Kuang

The paper develops a machine-learning model to predict how structural variants disrupt 3D chromatin structure in congenital heart disease and validates its predictions using induced pluripotent stem cells.

The paper integrates 4D Nucleome data to analyze 3D genome structure and function in human cells, revealing chromatin domains, loops, and interactions, and showing how genetic variants affect genome folding.

This study introduces novel methods for comparing chromatin contact maps and evaluates their performance using Hi-C data and in silico maps, highlighting the need for biologically informed approaches.

This study shows that reduced TBX5 dosage affects 3D chromatin organization in human cardiomyocytes, with cell-to-cell variation and partial protection by CTCF co-occupancy.

The paper uses sequence-based machine learning to predict 3D genome contact maps in bonobos and chimpanzees, identifying divergent folding patterns and variants affecting genome structure.

The paper uses machine learning to infer 3D chromatin contact patterns across diverse human populations and identifies regions with significant 3D divergence not explained by sequence variation.

The paper introduces a deep learning framework, AkitaR, to study how chromatin-associated RNAs influence genome folding in HFFc6 cells, identifying known and novel RNAs involved in chromatin architecture.